Using HRAM Survey Analysis Combined with Rapid MS2 Data to Develop a Fragmentation Based Detection Workflow for Structure ID Acquisition Tim Stratton,1 Caroline Ding,1 Christoph Henrich,2 Hans Grensemann,2 Hans Pfaff,2 Martin Strohalm,2 Sebastian Kusch2

1Thermo Fisher Scientific, San Jose, CA, USA; 2Thermo Fisher Scientific, Bremen, Germany

2 Using HRAM Survey Analysis Combined with Rapid MS2 Data to Develop a Fragmentation Based Detection Workflow for Structure ID Acquisition

Using HRAM Survey Analysis Combined with Rapid MS2 Data to Develop a Fragmentation Based Detection Workflow for Structure ID Acquisition

Tim Stratton1, Caroline Ding1, Christoph Henrich2, Hans Grensemann2, Hans Pfaff2, Martin Strohalm2, Sebastian Kusch2 1Thermo Fisher Scientific, San Jose, CA 2Thermo Fisher Scientific, Bremen, Germany

Conclusion Utilizing a rapid HCD MS2 survey injection enabled by dynamic scan management along with the mixed-fragmentation mechanism MSn approach possible on the Orbitrap Fusion mass spectrometer we have demonstrated the ability to detect and identify metabolites from multiple parents in complex biological samples.

Combining these acquisition techniques with a customizable workflow for data processing in Compound Discoverer software we have demonstrated:

•  The usefulness of high-scan-rate accurate-mass HCD MS2 acquisition combined with orthogonal metabolite detection techniques for peak finding.

•  Combined fragmentation mechanisms in MS3 (combining HCD and CID) to access more structure determination information.

Overview Purpose: Demonstrate a workflow for metabolite detection and structure elucidation combining HRAM (high-resolution, accurate-mass) HCD MS2 with a new customizable processing workflow using orthogonal metabolite detection tools to direct multiple fragmentation mode MSn acquisition. Methods: Samples of human urine, post-kidney operation, were analyzed by rapid HRAM HCD MS2 for peak detection. Potential metabolite peaks were acquired by multiple-fragment-mechanism MSn for structure identification where an HCD MS2 scan was followed by multiple CID MS3 fragmentation scans. Results: Multiple metabolites for each of four co- and serially administered drugs commonly used during surgery were detected and identified.

Introduction The complexity of biological matrices makes the task of metabolite detection difficult. Identification of which components in a sample or series of samples are of interest is the first step in the final determination of the metabolites’ structure. Here we demonstrate the use of fast scanning HCD MS2 combined with a novel data processing workflow to determine peaks of interest in samples of human urine following kidney surgery where multiple drugs were administered (anesthetics, analgesics, antibiotics).   Subsequent to the detection of components of interest, we have demonstrated the use of HRAM MSn acquisition combining different fragmentation techniques to improve metabolite structure identification. Acquisition combined an MS2 scan using HCD to generate multiple fragments from a high energy event followed by multiple MS3 scans using CID fragmentation to determine fragment heritage and structure.

Methods Sample Preparation

Samples of human urine were acquired post kidney surgery (five samples over the first 12 hours after surgery). Samples (10 mL aliquots) were prepared for analysis by solid phase extraction using Thermo Scientific™ Hypersep C18 SPE columns, (50mg), by washing with 2 volumes of water followed by elution with one volume each of acetonitrile and methanol. Eluates were evaporated to dryness under a stream of nitrogen gas and reconstituted in 1 mL of 90:10 water:acetonitrile for injection.

Liquid Chromatography Column: Thermo Scientific™ Accucore™ C18 100 x 2.1 particle (µ) Injection volume: 2 µL Flow rate: 300 µl/min. LC Gradient: MP A: H2O/0.1% formic acid. MP B: ACN/0.1% formic acid

Results Identifying Peaks of Interest in Survey Analysis Samples were initially acquired using a high-resolution full scan (60,000 FWHM @ m/z 200) and multiple data-dependent HCD MS2 fragmentation scans. The instrument was operated with a cycle time of 600 msec which achieved an average of 4-5 MS2 events per full scan. This cycle time, combined with dynamic exclusion, allowed for acquisition on large numbers of co-eluting components in the matrix. Internal calibration was also used during acquisition to assure that mass accuracy was less than 1 ppm. Internal calibration was achieved using a novel source calibration system which requires no calibration solution to be infused.

To process the survey injections, a series of phase I and phase II modifications were included in the metabolite generator (a total of 32) and the node was allowed to predict transformations of up to 3 sequential steps including 1 or 2 steps of dealkylation. Redundant predictions were removed but the remaining list contained 513 possible metabolites for ciprofloxacin, 622 for fentanyl, 348 for hydrocodone, and 105 for propofol. Peaks detected were confirmed by comparison with fragmentation and mass defect filters before inclusion for MSn acquisition. Multiple metabolites were detected (Figure 3) based on this approach.

All trademarks are the property of Thermo Fisher Scientific and its subsidiaries. This information is not intended to encourage use of these products in any manners that might infringe the intellectual property rights of others.

FIGURE 1. Mass Accuracy Scan-to-Scan: Hydrocodone in Urine #2

FIGURE 3. Top 50 Potential Metabolites of Propofol, Fentanyl, Hydrocodone, and Ciprofloxacin in Human Urine Sample #2

FIGURE 5. Fragmentation of Reduction + Bisoxidation of Ciprofloxacin. Acquisition of Combined Fragmentation Mechanism MSn Data With the list of potential metabolites from the processing of the survey injections, samples were injected using a HRAM MSn acquisition method (Figure 4) which combined HCD MS2 with CID MS3. The ability to combine fragmentation mechanisms was unique and useful for metabolite structure identification. We made use of the wide fragment mass range of the higher energy HCD mechanism for MS2 to generate a wide range of fragments to select for subsequent CID MS3. The lower energy of the collisional dissociation MS3 event allowed for higher sensitivity and the ability to determine fragment heritage (which MS2 fragment ion comes from which), this is useful in determining the site of modification as it allows for a more detailed analysis of the structure of the metabolite.

Mass Spectrometer Thermo Scientific™ Orbitrap Fusion™ Tribrid™ Mass Spectrometer Ion source: Easy-IC Ionization mode: ESI positive Sheath gas flow rate: 45 units N2 Auxiliary gas flow rate: 15 units N2 Spray voltage (KV): +3.5 Capillary temp (oC): 320 S-lens RF level: 60.0 Heater temp (oC): 275

Propofol C12H18O

MW. 178.13577

Fentanyl C22H28N2O

MW. 336.22016

Ciprofloxacin C17H18N3O3F

MW. 331.13322

Hydrocodone C18H21NO3

MW. 299.15214

Processing Workflow for Metabolite Detection The high-resolution, accurate-mass survey injections were processed using the novel workflow-based Thermo Scientific™ Compound Discoverer™ software allowing for a custom processing approach. The workflow editor (Figure 2) allows a custom workflow to be created to process any data. In this example, metabolites of each of four different drugs present were detected with a workflow that combined multiple orthogonal detection approaches including a combinatorial metabolite search with standard multiple mass defect filter and fragment searching.

The combinatorial approach uses a list of known single-step metabolic transformations along with simple user inputs to assemble a list of potential metabolite masses. This process also includes the generation of dealkylation products (optional) with subsequent metabolism of these products. The list of biotransformations used is also adjusted to the elemental composition of the parent such that dehalogenation is not applied to structures that do not contain a halogen.

OH

N

N

O

N

O

O

O

NH

N N

O

FOH

O

FIGURE 4. Orbitrap Fusion Mass Spectrometer Method Editor for Combined HCD CID MS3 Fragmentation Acquisition.

Time (min) MP A (%) MP B (%) 0.0 98 2 0.5 98 2 13.0 5 95 13.8 5 95 14.0 98 2 15.0 98 2

FIGURE 2. Processing Workflow Editor.

RT: 3.84 - 4.18

3.85 3.90 3.95 4.00 4.05 4.10 4.15Time (min)

0

10

20

30

40

50

60

70

80

90

100R

elat

ive

Abun

danc

e4.02 NL:

1.23E7Base Peak m/z= 300.1579-300.1609 F: ms MS PB_U2_OT-MS3_01

RT: 3.84 - 4.18

3.85 3.90 3.95 4.00 4.05 4.10 4.15Time (min)

0

10

20

30

40

50

60

70

80

90

100R

elat

ive

Abun

danc

e4.02 NL:

1.23E7Base Peak m/z= 300.1579-300.1609 F: ms MS PB_U2_OT-MS3_01

300.

1592

7 (-

0.50

pp

m)

300.

1594

8 (0

.20

ppm

) 30

0.15

933

(-0.

30

ppm

) 30

0.15

939

(-0.

10

ppm

) 30

0.15

952

(0.3

3 pp

m)

300.

1593

9 (-

0.10

pp

m)

300.

1595

8 (0

.53

ppm

) 30

0.15

952

(0.3

3 pp

m)

300.

1594

5 (0

.01

ppm

)

300.

1595

2 (0

.33

ppm

) 30

0.15

955

(0.4

3 pp

m)

300.

1595

2 (0

.33

ppm

) 30

0.15

948

(0.2

0 pp

m)

300.

1594

8 (0

.20

ppm

)

Average accuracy: 0.14 ppm

N

O

O

O

Hydrocodone m/z =

300.15942

Structure Identification Using HRAM MSn

The accurate-mass fragmentation spectral trees acquired were interpreted using theoretical fragmentation of the parent drug from which the metabolite was believed to be derived. For peaks conforming to an expected set of single-step enzymatic biotransformations, fragmentation was interpreted using the parent theoretical fragments and only the subset of single-step biotransformations. In many cases, more than one set of biotransformations could explain an observed peak and the interpretation of the peak using each set allowed for easier determination of the correct biotransformation steps involved and subsequently the correct location(s) for the modification(s). One example of this interpretation is shown in Figure 5 for a reduction / bisoxidative metabolite of ciprofloxacin.

HCD MS2

HCD MS2 CID MS3

362291

NH

N N

O

FOH

O

OH

O

3Thermo Scientific Poster Note • PN ASMS13_WP259_TStratton_e 06/13S

Using HRAM Survey Analysis Combined with Rapid MS2 Data to Develop a Fragmentation Based Detection Workflow for Structure ID Acquisition

Tim Stratton1, Caroline Ding1, Christoph Henrich2, Hans Grensemann2, Hans Pfaff2, Martin Strohalm2, Sebastian Kusch2 1Thermo Fisher Scientific, San Jose, CA 2Thermo Fisher Scientific, Bremen, Germany

Conclusion Utilizing a rapid HCD MS2 survey injection enabled by dynamic scan management along with the mixed-fragmentation mechanism MSn approach possible on the Orbitrap Fusion mass spectrometer we have demonstrated the ability to detect and identify metabolites from multiple parents in complex biological samples.

Combining these acquisition techniques with a customizable workflow for data processing in Compound Discoverer software we have demonstrated:

•  The usefulness of high-scan-rate accurate-mass HCD MS2 acquisition combined with orthogonal metabolite detection techniques for peak finding.

•  Combined fragmentation mechanisms in MS3 (combining HCD and CID) to access more structure determination information.

Overview Purpose: Demonstrate a workflow for metabolite detection and structure elucidation combining HRAM (high-resolution, accurate-mass) HCD MS2 with a new customizable processing workflow using orthogonal metabolite detection tools to direct multiple fragmentation mode MSn acquisition. Methods: Samples of human urine, post-kidney operation, were analyzed by rapid HRAM HCD MS2 for peak detection. Potential metabolite peaks were acquired by multiple-fragment-mechanism MSn for structure identification where an HCD MS2 scan was followed by multiple CID MS3 fragmentation scans. Results: Multiple metabolites for each of four co- and serially administered drugs commonly used during surgery were detected and identified.

Introduction The complexity of biological matrices makes the task of metabolite detection difficult. Identification of which components in a sample or series of samples are of interest is the first step in the final determination of the metabolites’ structure. Here we demonstrate the use of fast scanning HCD MS2 combined with a novel data processing workflow to determine peaks of interest in samples of human urine following kidney surgery where multiple drugs were administered (anesthetics, analgesics, antibiotics).   Subsequent to the detection of components of interest, we have demonstrated the use of HRAM MSn acquisition combining different fragmentation techniques to improve metabolite structure identification. Acquisition combined an MS2 scan using HCD to generate multiple fragments from a high energy event followed by multiple MS3 scans using CID fragmentation to determine fragment heritage and structure.

Methods Sample Preparation

Samples of human urine were acquired post kidney surgery (five samples over the first 12 hours after surgery). Samples (10 mL aliquots) were prepared for analysis by solid phase extraction using Thermo Scientific™ Hypersep C18 SPE columns, (50mg), by washing with 2 volumes of water followed by elution with one volume each of acetonitrile and methanol. Eluates were evaporated to dryness under a stream of nitrogen gas and reconstituted in 1 mL of 90:10 water:acetonitrile for injection.

Liquid Chromatography Column: Thermo Scientific™ Accucore™ C18 100 x 2.1 particle (µ) Injection volume: 2 µL Flow rate: 300 µl/min. LC Gradient: MP A: H2O/0.1% formic acid. MP B: ACN/0.1% formic acid

Results Identifying Peaks of Interest in Survey Analysis Samples were initially acquired using a high-resolution full scan (60,000 FWHM @ m/z 200) and multiple data-dependent HCD MS2 fragmentation scans. The instrument was operated with a cycle time of 600 msec which achieved an average of 4-5 MS2 events per full scan. This cycle time, combined with dynamic exclusion, allowed for acquisition on large numbers of co-eluting components in the matrix. Internal calibration was also used during acquisition to assure that mass accuracy was less than 1 ppm. Internal calibration was achieved using a novel source calibration system which requires no calibration solution to be infused.

To process the survey injections, a series of phase I and phase II modifications were included in the metabolite generator (a total of 32) and the node was allowed to predict transformations of up to 3 sequential steps including 1 or 2 steps of dealkylation. Redundant predictions were removed but the remaining list contained 513 possible metabolites for ciprofloxacin, 622 for fentanyl, 348 for hydrocodone, and 105 for propofol. Peaks detected were confirmed by comparison with fragmentation and mass defect filters before inclusion for MSn acquisition. Multiple metabolites were detected (Figure 3) based on this approach.

All trademarks are the property of Thermo Fisher Scientific and its subsidiaries. This information is not intended to encourage use of these products in any manners that might infringe the intellectual property rights of others.

FIGURE 1. Mass Accuracy Scan-to-Scan: Hydrocodone in Urine #2

FIGURE 3. Top 50 Potential Metabolites of Propofol, Fentanyl, Hydrocodone, and Ciprofloxacin in Human Urine Sample #2

FIGURE 5. Fragmentation of Reduction + Bisoxidation of Ciprofloxacin. Acquisition of Combined Fragmentation Mechanism MSn Data With the list of potential metabolites from the processing of the survey injections, samples were injected using a HRAM MSn acquisition method (Figure 4) which combined HCD MS2 with CID MS3. The ability to combine fragmentation mechanisms was unique and useful for metabolite structure identification. We made use of the wide fragment mass range of the higher energy HCD mechanism for MS2 to generate a wide range of fragments to select for subsequent CID MS3. The lower energy of the collisional dissociation MS3 event allowed for higher sensitivity and the ability to determine fragment heritage (which MS2 fragment ion comes from which), this is useful in determining the site of modification as it allows for a more detailed analysis of the structure of the metabolite.

Mass Spectrometer Thermo Scientific™ Orbitrap Fusion™ Tribrid™ Mass Spectrometer Ion source: Easy-IC Ionization mode: ESI positive Sheath gas flow rate: 45 units N2 Auxiliary gas flow rate: 15 units N2 Spray voltage (KV): +3.5 Capillary temp (oC): 320 S-lens RF level: 60.0 Heater temp (oC): 275

Propofol C12H18O

MW. 178.13577

Fentanyl C22H28N2O

MW. 336.22016

Ciprofloxacin C17H18N3O3F

MW. 331.13322

Hydrocodone C18H21NO3

MW. 299.15214

Processing Workflow for Metabolite Detection The high-resolution, accurate-mass survey injections were processed using the novel workflow-based Thermo Scientific™ Compound Discoverer™ software allowing for a custom processing approach. The workflow editor (Figure 2) allows a custom workflow to be created to process any data. In this example, metabolites of each of four different drugs present were detected with a workflow that combined multiple orthogonal detection approaches including a combinatorial metabolite search with standard multiple mass defect filter and fragment searching.

The combinatorial approach uses a list of known single-step metabolic transformations along with simple user inputs to assemble a list of potential metabolite masses. This process also includes the generation of dealkylation products (optional) with subsequent metabolism of these products. The list of biotransformations used is also adjusted to the elemental composition of the parent such that dehalogenation is not applied to structures that do not contain a halogen.

OH

N

N

O

N

O

O

O

NH

N N

O

FOH

O

FIGURE 4. Orbitrap Fusion Mass Spectrometer Method Editor for Combined HCD CID MS3 Fragmentation Acquisition.

Time (min) MP A (%) MP B (%) 0.0 98 2 0.5 98 2 13.0 5 95 13.8 5 95 14.0 98 2 15.0 98 2

FIGURE 2. Processing Workflow Editor.

RT: 3.84 - 4.18

3.85 3.90 3.95 4.00 4.05 4.10 4.15Time (min)

0

10

20

30

40

50

60

70

80

90

100

Rel

ativ

e Ab

unda

nce

4.02 NL:1.23E7Base Peak m/z= 300.1579-300.1609 F: ms MS PB_U2_OT-MS3_01

RT: 3.84 - 4.18

3.85 3.90 3.95 4.00 4.05 4.10 4.15Time (min)

0

10

20

30

40

50

60

70

80

90

100

Rel

ativ

e Ab

unda

nce

4.02 NL:1.23E7Base Peak m/z= 300.1579-300.1609 F: ms MS PB_U2_OT-MS3_01

300.

1592

7 (-

0.50

pp

m)

300.

1594

8 (0

.20

ppm

) 30

0.15

933

(-0.

30

ppm

) 30

0.15

939

(-0.

10

ppm

) 30

0.15

952

(0.3

3 pp

m)

300.

1593

9 (-

0.10

pp

m)

300.

1595

8 (0

.53

ppm

) 30

0.15

952

(0.3

3 pp

m)

300.

1594

5 (0

.01

ppm

)

300.

1595

2 (0

.33

ppm

) 30

0.15

955

(0.4

3 pp

m)

300.

1595

2 (0

.33

ppm

) 30

0.15

948

(0.2

0 pp

m)

300.

1594

8 (0

.20

ppm

)

Average accuracy: 0.14 ppm

N

O

O

O

Hydrocodone m/z =

300.15942

Structure Identification Using HRAM MSn

The accurate-mass fragmentation spectral trees acquired were interpreted using theoretical fragmentation of the parent drug from which the metabolite was believed to be derived. For peaks conforming to an expected set of single-step enzymatic biotransformations, fragmentation was interpreted using the parent theoretical fragments and only the subset of single-step biotransformations. In many cases, more than one set of biotransformations could explain an observed peak and the interpretation of the peak using each set allowed for easier determination of the correct biotransformation steps involved and subsequently the correct location(s) for the modification(s). One example of this interpretation is shown in Figure 5 for a reduction / bisoxidative metabolite of ciprofloxacin.

HCD MS2

HCD MS2 CID MS3

362291

NH

N N

O

FOH

O

OH

O

4 Using HRAM Survey Analysis Combined with Rapid MS2 Data to Develop a Fragmentation Based Detection Workflow for Structure ID Acquisition

Using HRAM Survey Analysis Combined with Rapid MS2 Data to Develop a Fragmentation Based Detection Workflow for Structure ID Acquisition

Tim Stratton1, Caroline Ding1, Christoph Henrich2, Hans Grensemann2, Hans Pfaff2, Martin Strohalm2, Sebastian Kusch2 1Thermo Fisher Scientific, San Jose, CA 2Thermo Fisher Scientific, Bremen, Germany

Conclusion Utilizing a rapid HCD MS2 survey injection enabled by dynamic scan management along with the mixed-fragmentation mechanism MSn approach possible on the Orbitrap Fusion mass spectrometer we have demonstrated the ability to detect and identify metabolites from multiple parents in complex biological samples.

Combining these acquisition techniques with a customizable workflow for data processing in Compound Discoverer software we have demonstrated:

•  The usefulness of high-scan-rate accurate-mass HCD MS2 acquisition combined with orthogonal metabolite detection techniques for peak finding.

•  Combined fragmentation mechanisms in MS3 (combining HCD and CID) to access more structure determination information.

Overview Purpose: Demonstrate a workflow for metabolite detection and structure elucidation combining HRAM (high-resolution, accurate-mass) HCD MS2 with a new customizable processing workflow using orthogonal metabolite detection tools to direct multiple fragmentation mode MSn acquisition. Methods: Samples of human urine, post-kidney operation, were analyzed by rapid HRAM HCD MS2 for peak detection. Potential metabolite peaks were acquired by multiple-fragment-mechanism MSn for structure identification where an HCD MS2 scan was followed by multiple CID MS3 fragmentation scans. Results: Multiple metabolites for each of four co- and serially administered drugs commonly used during surgery were detected and identified.

Introduction The complexity of biological matrices makes the task of metabolite detection difficult. Identification of which components in a sample or series of samples are of interest is the first step in the final determination of the metabolites’ structure. Here we demonstrate the use of fast scanning HCD MS2 combined with a novel data processing workflow to determine peaks of interest in samples of human urine following kidney surgery where multiple drugs were administered (anesthetics, analgesics, antibiotics).   Subsequent to the detection of components of interest, we have demonstrated the use of HRAM MSn acquisition combining different fragmentation techniques to improve metabolite structure identification. Acquisition combined an MS2 scan using HCD to generate multiple fragments from a high energy event followed by multiple MS3 scans using CID fragmentation to determine fragment heritage and structure.

Methods Sample Preparation

Samples of human urine were acquired post kidney surgery (five samples over the first 12 hours after surgery). Samples (10 mL aliquots) were prepared for analysis by solid phase extraction using Thermo Scientific™ Hypersep C18 SPE columns, (50mg), by washing with 2 volumes of water followed by elution with one volume each of acetonitrile and methanol. Eluates were evaporated to dryness under a stream of nitrogen gas and reconstituted in 1 mL of 90:10 water:acetonitrile for injection.

Liquid Chromatography Column: Thermo Scientific™ Accucore™ C18 100 x 2.1 particle (µ) Injection volume: 2 µL Flow rate: 300 µl/min. LC Gradient: MP A: H2O/0.1% formic acid. MP B: ACN/0.1% formic acid

Results Identifying Peaks of Interest in Survey Analysis Samples were initially acquired using a high-resolution full scan (60,000 FWHM @ m/z 200) and multiple data-dependent HCD MS2 fragmentation scans. The instrument was operated with a cycle time of 600 msec which achieved an average of 4-5 MS2 events per full scan. This cycle time, combined with dynamic exclusion, allowed for acquisition on large numbers of co-eluting components in the matrix. Internal calibration was also used during acquisition to assure that mass accuracy was less than 1 ppm. Internal calibration was achieved using a novel source calibration system which requires no calibration solution to be infused.

To process the survey injections, a series of phase I and phase II modifications were included in the metabolite generator (a total of 32) and the node was allowed to predict transformations of up to 3 sequential steps including 1 or 2 steps of dealkylation. Redundant predictions were removed but the remaining list contained 513 possible metabolites for ciprofloxacin, 622 for fentanyl, 348 for hydrocodone, and 105 for propofol. Peaks detected were confirmed by comparison with fragmentation and mass defect filters before inclusion for MSn acquisition. Multiple metabolites were detected (Figure 3) based on this approach.

All trademarks are the property of Thermo Fisher Scientific and its subsidiaries. This information is not intended to encourage use of these products in any manners that might infringe the intellectual property rights of others.

FIGURE 1. Mass Accuracy Scan-to-Scan: Hydrocodone in Urine #2

FIGURE 3. Top 50 Potential Metabolites of Propofol, Fentanyl, Hydrocodone, and Ciprofloxacin in Human Urine Sample #2

FIGURE 5. Fragmentation of Reduction + Bisoxidation of Ciprofloxacin. Acquisition of Combined Fragmentation Mechanism MSn Data With the list of potential metabolites from the processing of the survey injections, samples were injected using a HRAM MSn acquisition method (Figure 4) which combined HCD MS2 with CID MS3. The ability to combine fragmentation mechanisms was unique and useful for metabolite structure identification. We made use of the wide fragment mass range of the higher energy HCD mechanism for MS2 to generate a wide range of fragments to select for subsequent CID MS3. The lower energy of the collisional dissociation MS3 event allowed for higher sensitivity and the ability to determine fragment heritage (which MS2 fragment ion comes from which), this is useful in determining the site of modification as it allows for a more detailed analysis of the structure of the metabolite.

Mass Spectrometer Thermo Scientific™ Orbitrap Fusion™ Tribrid™ Mass Spectrometer Ion source: Easy-IC Ionization mode: ESI positive Sheath gas flow rate: 45 units N2 Auxiliary gas flow rate: 15 units N2 Spray voltage (KV): +3.5 Capillary temp (oC): 320 S-lens RF level: 60.0 Heater temp (oC): 275

Propofol C12H18O

MW. 178.13577

Fentanyl C22H28N2O

MW. 336.22016

Ciprofloxacin C17H18N3O3F

MW. 331.13322

Hydrocodone C18H21NO3

MW. 299.15214

Processing Workflow for Metabolite Detection The high-resolution, accurate-mass survey injections were processed using the novel workflow-based Thermo Scientific™ Compound Discoverer™ software allowing for a custom processing approach. The workflow editor (Figure 2) allows a custom workflow to be created to process any data. In this example, metabolites of each of four different drugs present were detected with a workflow that combined multiple orthogonal detection approaches including a combinatorial metabolite search with standard multiple mass defect filter and fragment searching.

The combinatorial approach uses a list of known single-step metabolic transformations along with simple user inputs to assemble a list of potential metabolite masses. This process also includes the generation of dealkylation products (optional) with subsequent metabolism of these products. The list of biotransformations used is also adjusted to the elemental composition of the parent such that dehalogenation is not applied to structures that do not contain a halogen.

OH

N

N

O

N

O

O

O

NH

N N

O

FOH

O

FIGURE 4. Orbitrap Fusion Mass Spectrometer Method Editor for Combined HCD CID MS3 Fragmentation Acquisition.

Time (min) MP A (%) MP B (%) 0.0 98 2 0.5 98 2 13.0 5 95 13.8 5 95 14.0 98 2 15.0 98 2

FIGURE 2. Processing Workflow Editor.

RT: 3.84 - 4.18

3.85 3.90 3.95 4.00 4.05 4.10 4.15Time (min)

0

10

20

30

40

50

60

70

80

90

100

Rel

ativ

e Ab

unda

nce

4.02 NL:1.23E7Base Peak m/z= 300.1579-300.1609 F: ms MS PB_U2_OT-MS3_01

RT: 3.84 - 4.18

3.85 3.90 3.95 4.00 4.05 4.10 4.15Time (min)

0

10

20

30

40

50

60

70

80

90

100

Rel

ativ

e Ab

unda

nce

4.02 NL:1.23E7Base Peak m/z= 300.1579-300.1609 F: ms MS PB_U2_OT-MS3_01

300.

1592

7 (-

0.50

pp

m)

300.

1594

8 (0

.20

ppm

) 30

0.15

933

(-0.

30

ppm

) 30

0.15

939

(-0.

10

ppm

) 30

0.15

952

(0.3

3 pp

m)

300.

1593

9 (-

0.10

pp

m)

300.

1595

8 (0

.53

ppm

) 30

0.15

952

(0.3

3 pp

m)

300.

1594

5 (0

.01

ppm

)

300.

1595

2 (0

.33

ppm

) 30

0.15

955

(0.4

3 pp

m)

300.

1595

2 (0

.33

ppm

) 30

0.15

948

(0.2

0 pp

m)

300.

1594

8 (0

.20

ppm

)

Average accuracy: 0.14 ppm

N

O

O

O

Hydrocodone m/z =

300.15942

Structure Identification Using HRAM MSn

The accurate-mass fragmentation spectral trees acquired were interpreted using theoretical fragmentation of the parent drug from which the metabolite was believed to be derived. For peaks conforming to an expected set of single-step enzymatic biotransformations, fragmentation was interpreted using the parent theoretical fragments and only the subset of single-step biotransformations. In many cases, more than one set of biotransformations could explain an observed peak and the interpretation of the peak using each set allowed for easier determination of the correct biotransformation steps involved and subsequently the correct location(s) for the modification(s). One example of this interpretation is shown in Figure 5 for a reduction / bisoxidative metabolite of ciprofloxacin.

HCD MS2

HCD MS2 CID MS3

362291

NH

N N

O

FOH

O

OH

O

5Thermo Scientific Poster Note • PN ASMS13_WP259_TStratton_e 06/13S

Using HRAM Survey Analysis Combined with Rapid MS2 Data to Develop a Fragmentation Based Detection Workflow for Structure ID Acquisition

Tim Stratton1, Caroline Ding1, Christoph Henrich2, Hans Grensemann2, Hans Pfaff2, Martin Strohalm2, Sebastian Kusch2 1Thermo Fisher Scientific, San Jose, CA 2Thermo Fisher Scientific, Bremen, Germany

Conclusion Utilizing a rapid HCD MS2 survey injection enabled by dynamic scan management along with the mixed-fragmentation mechanism MSn approach possible on the Orbitrap Fusion mass spectrometer we have demonstrated the ability to detect and identify metabolites from multiple parents in complex biological samples.

Combining these acquisition techniques with a customizable workflow for data processing in Compound Discoverer software we have demonstrated:

•  The usefulness of high-scan-rate accurate-mass HCD MS2 acquisition combined with orthogonal metabolite detection techniques for peak finding.

•  Combined fragmentation mechanisms in MS3 (combining HCD and CID) to access more structure determination information.

Overview Purpose: Demonstrate a workflow for metabolite detection and structure elucidation combining HRAM (high-resolution, accurate-mass) HCD MS2 with a new customizable processing workflow using orthogonal metabolite detection tools to direct multiple fragmentation mode MSn acquisition. Methods: Samples of human urine, post-kidney operation, were analyzed by rapid HRAM HCD MS2 for peak detection. Potential metabolite peaks were acquired by multiple-fragment-mechanism MSn for structure identification where an HCD MS2 scan was followed by multiple CID MS3 fragmentation scans. Results: Multiple metabolites for each of four co- and serially administered drugs commonly used during surgery were detected and identified.

Introduction The complexity of biological matrices makes the task of metabolite detection difficult. Identification of which components in a sample or series of samples are of interest is the first step in the final determination of the metabolites’ structure. Here we demonstrate the use of fast scanning HCD MS2 combined with a novel data processing workflow to determine peaks of interest in samples of human urine following kidney surgery where multiple drugs were administered (anesthetics, analgesics, antibiotics).   Subsequent to the detection of components of interest, we have demonstrated the use of HRAM MSn acquisition combining different fragmentation techniques to improve metabolite structure identification. Acquisition combined an MS2 scan using HCD to generate multiple fragments from a high energy event followed by multiple MS3 scans using CID fragmentation to determine fragment heritage and structure.

Methods Sample Preparation

Samples of human urine were acquired post kidney surgery (five samples over the first 12 hours after surgery). Samples (10 mL aliquots) were prepared for analysis by solid phase extraction using Thermo Scientific™ Hypersep C18 SPE columns, (50mg), by washing with 2 volumes of water followed by elution with one volume each of acetonitrile and methanol. Eluates were evaporated to dryness under a stream of nitrogen gas and reconstituted in 1 mL of 90:10 water:acetonitrile for injection.

Liquid Chromatography Column: Thermo Scientific™ Accucore™ C18 100 x 2.1 particle (µ) Injection volume: 2 µL Flow rate: 300 µl/min. LC Gradient: MP A: H2O/0.1% formic acid. MP B: ACN/0.1% formic acid

Results Identifying Peaks of Interest in Survey Analysis Samples were initially acquired using a high-resolution full scan (60,000 FWHM @ m/z 200) and multiple data-dependent HCD MS2 fragmentation scans. The instrument was operated with a cycle time of 600 msec which achieved an average of 4-5 MS2 events per full scan. This cycle time, combined with dynamic exclusion, allowed for acquisition on large numbers of co-eluting components in the matrix. Internal calibration was also used during acquisition to assure that mass accuracy was less than 1 ppm. Internal calibration was achieved using a novel source calibration system which requires no calibration solution to be infused.

To process the survey injections, a series of phase I and phase II modifications were included in the metabolite generator (a total of 32) and the node was allowed to predict transformations of up to 3 sequential steps including 1 or 2 steps of dealkylation. Redundant predictions were removed but the remaining list contained 513 possible metabolites for ciprofloxacin, 622 for fentanyl, 348 for hydrocodone, and 105 for propofol. Peaks detected were confirmed by comparison with fragmentation and mass defect filters before inclusion for MSn acquisition. Multiple metabolites were detected (Figure 3) based on this approach.

All trademarks are the property of Thermo Fisher Scientific and its subsidiaries. This information is not intended to encourage use of these products in any manners that might infringe the intellectual property rights of others.

FIGURE 1. Mass Accuracy Scan-to-Scan: Hydrocodone in Urine #2

FIGURE 3. Top 50 Potential Metabolites of Propofol, Fentanyl, Hydrocodone, and Ciprofloxacin in Human Urine Sample #2

FIGURE 5. Fragmentation of Reduction + Bisoxidation of Ciprofloxacin. Acquisition of Combined Fragmentation Mechanism MSn Data With the list of potential metabolites from the processing of the survey injections, samples were injected using a HRAM MSn acquisition method (Figure 4) which combined HCD MS2 with CID MS3. The ability to combine fragmentation mechanisms was unique and useful for metabolite structure identification. We made use of the wide fragment mass range of the higher energy HCD mechanism for MS2 to generate a wide range of fragments to select for subsequent CID MS3. The lower energy of the collisional dissociation MS3 event allowed for higher sensitivity and the ability to determine fragment heritage (which MS2 fragment ion comes from which), this is useful in determining the site of modification as it allows for a more detailed analysis of the structure of the metabolite.

Mass Spectrometer Thermo Scientific™ Orbitrap Fusion™ Tribrid™ Mass Spectrometer Ion source: Easy-IC Ionization mode: ESI positive Sheath gas flow rate: 45 units N2 Auxiliary gas flow rate: 15 units N2 Spray voltage (KV): +3.5 Capillary temp (oC): 320 S-lens RF level: 60.0 Heater temp (oC): 275

Propofol C12H18O

MW. 178.13577

Fentanyl C22H28N2O

MW. 336.22016

Ciprofloxacin C17H18N3O3F

MW. 331.13322

Hydrocodone C18H21NO3

MW. 299.15214

Processing Workflow for Metabolite Detection The high-resolution, accurate-mass survey injections were processed using the novel workflow-based Thermo Scientific™ Compound Discoverer™ software allowing for a custom processing approach. The workflow editor (Figure 2) allows a custom workflow to be created to process any data. In this example, metabolites of each of four different drugs present were detected with a workflow that combined multiple orthogonal detection approaches including a combinatorial metabolite search with standard multiple mass defect filter and fragment searching.

The combinatorial approach uses a list of known single-step metabolic transformations along with simple user inputs to assemble a list of potential metabolite masses. This process also includes the generation of dealkylation products (optional) with subsequent metabolism of these products. The list of biotransformations used is also adjusted to the elemental composition of the parent such that dehalogenation is not applied to structures that do not contain a halogen.

OH

N

N

O

N

O

O

O

NH

N N

O

FOH

O

FIGURE 4. Orbitrap Fusion Mass Spectrometer Method Editor for Combined HCD CID MS3 Fragmentation Acquisition.

Time (min) MP A (%) MP B (%) 0.0 98 2 0.5 98 2 13.0 5 95 13.8 5 95 14.0 98 2 15.0 98 2

FIGURE 2. Processing Workflow Editor.

RT: 3.84 - 4.18

3.85 3.90 3.95 4.00 4.05 4.10 4.15Time (min)

0

10

20

30

40

50

60

70

80

90

100

Rel

ativ

e Ab

unda

nce

4.02 NL:1.23E7Base Peak m/z= 300.1579-300.1609 F: ms MS PB_U2_OT-MS3_01

RT: 3.84 - 4.18

3.85 3.90 3.95 4.00 4.05 4.10 4.15Time (min)

0

10

20

30

40

50

60

70

80

90

100

Rel

ativ

e Ab

unda

nce

4.02 NL:1.23E7Base Peak m/z= 300.1579-300.1609 F: ms MS PB_U2_OT-MS3_01

300.

1592

7 (-

0.50

pp

m)

300.

1594

8 (0

.20

ppm

) 30

0.15

933

(-0.

30

ppm

) 30

0.15

939

(-0.

10

ppm

) 30

0.15

952

(0.3

3 pp

m)

300.

1593

9 (-

0.10

pp

m)

300.

1595

8 (0

.53

ppm

) 30

0.15

952

(0.3

3 pp

m)

300.

1594

5 (0

.01

ppm

)

300.

1595

2 (0

.33

ppm

) 30

0.15

955

(0.4

3 pp

m)

300.

1595

2 (0

.33

ppm

) 30

0.15

948

(0.2

0 pp

m)

300.

1594

8 (0

.20

ppm

)

Average accuracy: 0.14 ppm

N

O

O

O

Hydrocodone m/z =

300.15942

Structure Identification Using HRAM MSn

The accurate-mass fragmentation spectral trees acquired were interpreted using theoretical fragmentation of the parent drug from which the metabolite was believed to be derived. For peaks conforming to an expected set of single-step enzymatic biotransformations, fragmentation was interpreted using the parent theoretical fragments and only the subset of single-step biotransformations. In many cases, more than one set of biotransformations could explain an observed peak and the interpretation of the peak using each set allowed for easier determination of the correct biotransformation steps involved and subsequently the correct location(s) for the modification(s). One example of this interpretation is shown in Figure 5 for a reduction / bisoxidative metabolite of ciprofloxacin.

HCD MS2

HCD MS2 CID MS3

362291

NH

N N

O

FOH

O

OH

O

6 Using HRAM Survey Analysis Combined with Rapid MS2 Data to Develop a Fragmentation Based Detection Workflow for Structure ID Acquisition

Using HRAM Survey Analysis Combined with Rapid MS2 Data to Develop a Fragmentation Based Detection Workflow for Structure ID Acquisition

Tim Stratton1, Caroline Ding1, Christoph Henrich2, Hans Grensemann2, Hans Pfaff2, Martin Strohalm2, Sebastian Kusch2 1Thermo Fisher Scientific, San Jose, CA 2Thermo Fisher Scientific, Bremen, Germany

Conclusion Utilizing a rapid HCD MS2 survey injection enabled by dynamic scan management along with the mixed-fragmentation mechanism MSn approach possible on the Orbitrap Fusion mass spectrometer we have demonstrated the ability to detect and identify metabolites from multiple parents in complex biological samples.

Combining these acquisition techniques with a customizable workflow for data processing in Compound Discoverer software we have demonstrated:

•  The usefulness of high-scan-rate accurate-mass HCD MS2 acquisition combined with orthogonal metabolite detection techniques for peak finding.

•  Combined fragmentation mechanisms in MS3 (combining HCD and CID) to access more structure determination information.

Overview Purpose: Demonstrate a workflow for metabolite detection and structure elucidation combining HRAM (high-resolution, accurate-mass) HCD MS2 with a new customizable processing workflow using orthogonal metabolite detection tools to direct multiple fragmentation mode MSn acquisition. Methods: Samples of human urine, post-kidney operation, were analyzed by rapid HRAM HCD MS2 for peak detection. Potential metabolite peaks were acquired by multiple-fragment-mechanism MSn for structure identification where an HCD MS2 scan was followed by multiple CID MS3 fragmentation scans. Results: Multiple metabolites for each of four co- and serially administered drugs commonly used during surgery were detected and identified.

Introduction The complexity of biological matrices makes the task of metabolite detection difficult. Identification of which components in a sample or series of samples are of interest is the first step in the final determination of the metabolites’ structure. Here we demonstrate the use of fast scanning HCD MS2 combined with a novel data processing workflow to determine peaks of interest in samples of human urine following kidney surgery where multiple drugs were administered (anesthetics, analgesics, antibiotics).   Subsequent to the detection of components of interest, we have demonstrated the use of HRAM MSn acquisition combining different fragmentation techniques to improve metabolite structure identification. Acquisition combined an MS2 scan using HCD to generate multiple fragments from a high energy event followed by multiple MS3 scans using CID fragmentation to determine fragment heritage and structure.

Methods Sample Preparation

Samples of human urine were acquired post kidney surgery (five samples over the first 12 hours after surgery). Samples (10 mL aliquots) were prepared for analysis by solid phase extraction using Thermo Scientific™ Hypersep C18 SPE columns, (50mg), by washing with 2 volumes of water followed by elution with one volume each of acetonitrile and methanol. Eluates were evaporated to dryness under a stream of nitrogen gas and reconstituted in 1 mL of 90:10 water:acetonitrile for injection.

Liquid Chromatography Column: Thermo Scientific™ Accucore™ C18 100 x 2.1 particle (µ) Injection volume: 2 µL Flow rate: 300 µl/min. LC Gradient: MP A: H2O/0.1% formic acid. MP B: ACN/0.1% formic acid

Results Identifying Peaks of Interest in Survey Analysis Samples were initially acquired using a high-resolution full scan (60,000 FWHM @ m/z 200) and multiple data-dependent HCD MS2 fragmentation scans. The instrument was operated with a cycle time of 600 msec which achieved an average of 4-5 MS2 events per full scan. This cycle time, combined with dynamic exclusion, allowed for acquisition on large numbers of co-eluting components in the matrix. Internal calibration was also used during acquisition to assure that mass accuracy was less than 1 ppm. Internal calibration was achieved using a novel source calibration system which requires no calibration solution to be infused.

To process the survey injections, a series of phase I and phase II modifications were included in the metabolite generator (a total of 32) and the node was allowed to predict transformations of up to 3 sequential steps including 1 or 2 steps of dealkylation. Redundant predictions were removed but the remaining list contained 513 possible metabolites for ciprofloxacin, 622 for fentanyl, 348 for hydrocodone, and 105 for propofol. Peaks detected were confirmed by comparison with fragmentation and mass defect filters before inclusion for MSn acquisition. Multiple metabolites were detected (Figure 3) based on this approach.

All trademarks are the property of Thermo Fisher Scientific and its subsidiaries. This information is not intended to encourage use of these products in any manners that might infringe the intellectual property rights of others.

FIGURE 1. Mass Accuracy Scan-to-Scan: Hydrocodone in Urine #2

FIGURE 3. Top 50 Potential Metabolites of Propofol, Fentanyl, Hydrocodone, and Ciprofloxacin in Human Urine Sample #2

FIGURE 5. Fragmentation of Reduction + Bisoxidation of Ciprofloxacin. Acquisition of Combined Fragmentation Mechanism MSn Data With the list of potential metabolites from the processing of the survey injections, samples were injected using a HRAM MSn acquisition method (Figure 4) which combined HCD MS2 with CID MS3. The ability to combine fragmentation mechanisms was unique and useful for metabolite structure identification. We made use of the wide fragment mass range of the higher energy HCD mechanism for MS2 to generate a wide range of fragments to select for subsequent CID MS3. The lower energy of the collisional dissociation MS3 event allowed for higher sensitivity and the ability to determine fragment heritage (which MS2 fragment ion comes from which), this is useful in determining the site of modification as it allows for a more detailed analysis of the structure of the metabolite.

Mass Spectrometer Thermo Scientific™ Orbitrap Fusion™ Tribrid™ Mass Spectrometer Ion source: Easy-IC Ionization mode: ESI positive Sheath gas flow rate: 45 units N2 Auxiliary gas flow rate: 15 units N2 Spray voltage (KV): +3.5 Capillary temp (oC): 320 S-lens RF level: 60.0 Heater temp (oC): 275

Propofol C12H18O

MW. 178.13577

Fentanyl C22H28N2O

MW. 336.22016

Ciprofloxacin C17H18N3O3F

MW. 331.13322

Hydrocodone C18H21NO3

MW. 299.15214

Processing Workflow for Metabolite Detection The high-resolution, accurate-mass survey injections were processed using the novel workflow-based Thermo Scientific™ Compound Discoverer™ software allowing for a custom processing approach. The workflow editor (Figure 2) allows a custom workflow to be created to process any data. In this example, metabolites of each of four different drugs present were detected with a workflow that combined multiple orthogonal detection approaches including a combinatorial metabolite search with standard multiple mass defect filter and fragment searching.

The combinatorial approach uses a list of known single-step metabolic transformations along with simple user inputs to assemble a list of potential metabolite masses. This process also includes the generation of dealkylation products (optional) with subsequent metabolism of these products. The list of biotransformations used is also adjusted to the elemental composition of the parent such that dehalogenation is not applied to structures that do not contain a halogen.

OH

N

N

O

N

O

O

O

NH

N N

O

FOH

O

FIGURE 4. Orbitrap Fusion Mass Spectrometer Method Editor for Combined HCD CID MS3 Fragmentation Acquisition.

Time (min) MP A (%) MP B (%) 0.0 98 2 0.5 98 2 13.0 5 95 13.8 5 95 14.0 98 2 15.0 98 2

FIGURE 2. Processing Workflow Editor.

RT: 3.84 - 4.18

3.85 3.90 3.95 4.00 4.05 4.10 4.15Time (min)

0

10

20

30

40

50

60

70

80

90

100

Rel

ativ

e Ab

unda

nce

4.02 NL:1.23E7Base Peak m/z= 300.1579-300.1609 F: ms MS PB_U2_OT-MS3_01

RT: 3.84 - 4.18

3.85 3.90 3.95 4.00 4.05 4.10 4.15Time (min)

0

10

20

30

40

50

60

70

80

90

100

Rel

ativ

e Ab

unda

nce

4.02 NL:1.23E7Base Peak m/z= 300.1579-300.1609 F: ms MS PB_U2_OT-MS3_01

300.

1592

7 (-

0.50

pp

m)

300.

1594

8 (0

.20

ppm

) 30

0.15

933

(-0.

30

ppm

) 30

0.15

939

(-0.

10

ppm

) 30

0.15

952

(0.3

3 pp

m)

300.

1593

9 (-

0.10

pp

m)

300.

1595

8 (0

.53

ppm

) 30

0.15

952

(0.3

3 pp

m)

300.

1594

5 (0

.01

ppm

)

300.

1595

2 (0

.33

ppm

) 30

0.15

955

(0.4

3 pp

m)

300.

1595

2 (0

.33

ppm

) 30

0.15

948

(0.2

0 pp

m)

300.

1594

8 (0

.20

ppm

)

Average accuracy: 0.14 ppm

N

O

O

O

Hydrocodone m/z =

300.15942

Structure Identification Using HRAM MSn

The accurate-mass fragmentation spectral trees acquired were interpreted using theoretical fragmentation of the parent drug from which the metabolite was believed to be derived. For peaks conforming to an expected set of single-step enzymatic biotransformations, fragmentation was interpreted using the parent theoretical fragments and only the subset of single-step biotransformations. In many cases, more than one set of biotransformations could explain an observed peak and the interpretation of the peak using each set allowed for easier determination of the correct biotransformation steps involved and subsequently the correct location(s) for the modification(s). One example of this interpretation is shown in Figure 5 for a reduction / bisoxidative metabolite of ciprofloxacin.

HCD MS2

HCD MS2 CID MS3

362291

NH

N N

O

FOH

O

OH

O

ASMS13_WP259_TStratton_E 06/13S

Africa-Other +27 11 570 1840Australia +61 3 9757 4300Austria +43 1 333 50 34 0Belgium +32 53 73 42 41Canada +1 800 530 8447China +86 10 8419 3588Denmark +45 70 23 62 60

Europe-Other +43 1 333 50 34 0Finland/Norway/Sweden +46 8 556 468 00France +33 1 60 92 48 00Germany +49 6103 408 1014India +91 22 6742 9434Italy +39 02 950 591

Japan +81 45 453 9100Latin America +1 561 688 8700Middle East +43 1 333 50 34 0Netherlands +31 76 579 55 55New Zealand +64 9 980 6700Russia/CIS +43 1 333 50 34 0South Africa +27 11 570 1840

Spain +34 914 845 965Switzerland +41 61 716 77 00UK +44 1442 233555USA +1 800 532 4752

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